Executive Guide to AutoML

Learn about AutoML, the opportunities and challenges that arise in attempting to automate machine learning, and how this automation affects your organization.

Introduction

• How AutoML is changing analytics teams
• What you should know?

1. Introducing AutoML

• What is AutoML?
• Understanding supervised machine learning on structured data
• Data engineering and ML Ops
• Understanding the ML lifecycle
• The challenge of ML problem definition

2. Stages in the ML Lifecycle

• Which phases have been automated most successfully?
• The challenge of automating data understanding
• What AutoML can and can't do during data prep
• AutoML's capabilities during the modeling phase
• Comparing model accuracy and business evaluation
• Monitoring and maintaining models

3. AutoML Options

• The AutoML vendor landscape
• Demonstrating AutoML with KNIME
• A metaphor for AutoML
• Advice for team composition

Conclusion

• Next steps